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This study aims at identifying an optimal set of features for predicting firms bankruptcy events in the current macroeconomic context. To this aim, among many financial features, we propose new country-specific factors which consider the macroeconomic conditions of the countries where firms operate. Our forecasting model is based on Support Vector Machines (SVMs), which are tools employed in supervised learning. Firstly, starting from a wide set of variables commonly used for bankruptcy prediction we assess the general effectiveness of SVMs also in comparison with the performances of other commonly used methods. Secondly, we try to improve the accuracy of forecasts by selecting optimal subsets of variables through a feature selection method. The results show that, in the current socio-economic context, the conjunct use of SVMs and the proposed feature selection technique significantly improves the accuracy of bankruptcy predictions compared to the performance of the other methods examined. Furthermore, we show that the proposed country-specific factors are relevant information for predicting the failure of firms and that most of the ratios proposed by Altman in 1968 are still relevant nowadays.

Short abstract: The authors describe the development of an innovative decision-support system that uses forecasting, discrete-event simulation, and optimization to provide an integrated approach to revenue and capacity management.

To date, the assessment of disability in older people is obtained utilizing a Comprehensive Geriatric Assessment (CGA). However, it is often difficult to understand which areas of CGA are most predictive of the disability. The aim of this study is to evaluate the possibility to early predictone year ahead-the disability level of a patient using Machine Leaning models. Methods Community-dwelling older people were enrolled in this study. CGA was made at baseline and at 1 year follow-up. After collecting input/independent variables (i.e. age, gender, schooling followed, Body Mass Index, information on smoking, polypharmacy, functional status, cognitive performance, depression, nutritional status), we performed two distinct Support Vector Machine models (SVMs) able to predict functional status one year ahead. In order to validate the choice of the model the results achieved with the SVMs were compared with the output produced by simple Linear Regression (LR) models. Results 218 patients (mean age =78.01; sd=7.85; Male= 39%) were recruited. The combination of the two SVMs is able to achieve a higher prediction accuracy (exceeding 80% instances correctly classified vs 67% instances correctly classified by the combination of the two linear regression models). Furthermore, SVMs are able to classify both the three categories, Self Sufficienty, Disability Risk and Disability, while linear regression model separates the population only in two groups (Self Sufficiency and Disability) without identifying the intermediate category (Disability Risk) which turns out to be the most critical one. Conclusions The development of such a model can contribute to the early detection of patients at risk of self-sufficiency loss.

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